{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows how to:  \n",
    "1. Load a counts matrix (10X Chromium data from human peripheral blood cells)\n",
    "2. Run the default Scrublet pipeline \n",
    "3. Check that doublet predictions make sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import scrublet as scr\n",
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = 'Arial'\n",
    "plt.rc('font', size=14)\n",
    "plt.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download 8k PBMC data set from 10X Genomics\n",
    "Download raw data from this link:\n",
    "http://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc8k/pbmc8k_filtered_gene_bc_matrices.tar.gz\n",
    "\n",
    "\n",
    "Or use wget:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-10-04 11:21:04--  http://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc8k/pbmc8k_filtered_gene_bc_matrices.tar.gz\n",
      "Resolving cf.10xgenomics.com (cf.10xgenomics.com)... 13.35.78.24, 13.35.78.116, 13.35.78.82, ...\n",
      "Connecting to cf.10xgenomics.com (cf.10xgenomics.com)|13.35.78.24|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 37558165 (36M) [application/x-tar]\n",
      "Saving to: ‘pbmc8k_filtered_gene_bc_matrices.tar.gz’\n",
      "\n",
      "pbmc8k_filtered_gen 100%[===================>]  35.82M  16.5MB/s    in 2.2s    \n",
      "\n",
      "2018-10-04 11:21:07 (16.5 MB/s) - ‘pbmc8k_filtered_gene_bc_matrices.tar.gz’ saved [37558165/37558165]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc8k/pbmc8k_filtered_gene_bc_matrices.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncompress:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tar xfz pbmc8k_filtered_gene_bc_matrices.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load counts matrix and gene list\n",
    "Load the raw counts matrix as a scipy sparse matrix with cells as rows and genes as columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts matrix shape: 8381 rows, 33694 columns\n",
      "Number of genes in gene list: 33694\n"
     ]
    }
   ],
   "source": [
    "input_dir = 'filtered_gene_bc_matrices/GRCh38/'\n",
    "counts_matrix = scipy.io.mmread(input_dir + '/matrix.mtx').T.tocsc()\n",
    "genes = np.array(scr.load_genes(input_dir + 'genes.tsv', delimiter='\\t', column=1))\n",
    "\n",
    "print('Counts matrix shape: {} rows, {} columns'.format(counts_matrix.shape[0], counts_matrix.shape[1]))\n",
    "print('Number of genes in gene list: {}'.format(len(genes)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initialize Scrublet object\n",
    "The relevant parameters are:\n",
    "- *expected_doublet_rate*: the expected fraction of transcriptomes that are doublets, typically 0.05-0.1. Results are not particularly sensitive to this parameter. For this example, the expected doublet rate comes from the Chromium User Guide: https://support.10xgenomics.com/permalink/3vzDu3zQjY0o2AqkkkI4CC\n",
    "- *sim_doublet_ratio*: the number of doublets to simulate, relative to the number of observed transcriptomes. This should be high enough that all doublet states are well-represented by simulated doublets. Setting it too high is computationally expensive. The default value is 2, though values as low as 0.5 give very similar results for the datasets that have been tested.\n",
    "- *n_neighbors*: Number of neighbors used to construct the KNN classifier of observed transcriptomes and simulated doublets. The default value of `round(0.5*sqrt(n_cells))` generally works well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scrub = scr.Scrublet(counts_matrix, expected_doublet_rate=0.06)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the default pipeline, which includes:\n",
    "1. Doublet simulation\n",
    "2. Normalization, gene filtering, rescaling, PCA\n",
    "3. Doublet score calculation \n",
    "4. Doublet score threshold detection and doublet calling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing...\n",
      "Simulating doublets...\n",
      "Embedding transcriptomes using PCA...\n",
      "Calculating doublet scores...\n",
      "Automatically set threshold at doublet score = 0.22\n",
      "Detected doublet rate = 4.3%\n",
      "Estimated detectable doublet fraction = 61.5%\n",
      "Overall doublet rate:\n",
      "\tExpected   = 6.0%\n",
      "\tEstimated  = 7.0%\n",
      "Elapsed time: 11.2 seconds\n"
     ]
    }
   ],
   "source": [
    "doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=2, \n",
    "                                                          min_cells=3, \n",
    "                                                          min_gene_variability_pctl=85, \n",
    "                                                          n_prin_comps=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot doublet score histograms  for observed transcriptomes and simulated doublets\n",
    "The simulated doublet histogram is typically bimodal. The left mode corresponds to \"embedded\" doublets generated by two cells with similar gene expression. The right mode corresponds to \"neotypic\" doublets, which are generated by cells with distinct gene expression (e.g., different cell types) and are expected to introduce more artifacts in downstream analyses. Scrublet can only detect neotypic doublets.  \n",
    "  \n",
    "To call doublets vs. singlets, we must set a threshold doublet score, ideally at the minimum between the two modes of the simulated doublet histogram. `scrub_doublets()` attempts to identify this point automatically and has done a good job in this example. However, if automatic threshold detection doesn't work well, you can adjust the threshold with the `call_doublets()` function. For example:\n",
    "```python\n",
    "scrub.call_doublets(threshold=0.25)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scrub.plot_histogram();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get 2-D embedding to visualize the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running UMAP...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "print('Running UMAP...')\n",
    "scrub.set_embedding('UMAP', scr.get_umap(scrub.manifold_obs_, 10, min_dist=0.3))\n",
    "\n",
    "# # Uncomment to run tSNE - slow\n",
    "# print('Running tSNE...')\n",
    "# scrub.set_embedding('tSNE', scr.get_tsne(scrub.manifold_obs_, angle=0.9))\n",
    "\n",
    "# # Uncomment to run force layout - slow\n",
    "# print('Running ForceAtlas2...')\n",
    "# scrub.set_embedding('FA', scr.get_force_layout(scrub.manifold_obs_, n_neighbors=5. n_iter=1000))\n",
    "    \n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot doublet predictions on 2-D embedding\n",
    "Predicted doublets should co-localize in distinct states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scrub.plot_embedding('UMAP', order_points=True);\n",
    "\n",
    "# scrub.plot_embedding('tSNE', order_points=True);\n",
    "# scrub.plot_embedding('FA', order_points=True);"
   ]
  }
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